import numpy as np 
from math import sqrt
from collections import Counter

class KNNClassifier:
	def __init__(self, k):
		'''初始化KNN分类器'''
		assert k >= 1, "k must be valid"
		self.k = k
		self._X_train = None
		self._y_train = None
	
	def fit(self, X_train, y_train):
		'''根据训练数据集X_train和y_train训练KNN分类器'''
		assert X_train.shape[0] == y_train.shape[0], "the size of X_train must equal to the size of y_train"
		assert self.k <= X_train.shape[0], "the size of X_train must be at least k"
	
		self._X_train = X_train
		self._y_train = y_train
		return self

	def predict(self, X_predict):
		'''给特定待预测数据集X_predict,返回表示X_predict的结果的向量'''
		assert self._X_train is not None and self._y_train is not None, "must fit before predict!"
		assert X_predict.shape[1] == self._X_train.shape[1], "the feature number of X_predict must be equal to X_train"
		
		y_predict = [self._predict(x) for x in X_predict]
		return np.array(y_predict)

	def _predict(self, x):
		#给定一个待预测数据x，返回x_predict的预测结果值
		assert x.shape[0] == self._X_train.shape[1], "the feature number of x must be equal to X_train"
        
		distances = [sqrt(np.sum((x_train - x)**2)) for x_train in self._X_train]
		nearest = np.argsort(distances)

		topk_y =[self._y_train[i] for i in nearest[:self.k]]
		votes = Counter(topk_y)
		return votes.most_common(1)[0][0]	

	def __repr__(self):
		return "KNN(k=%d)" % self.k